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Since the early 1980s, in southern China, evergreen broad-leaved forests have been replaced by Chinese fir plantations on a large scale. By analyzing the dynamic change characteristics of the landscape pattern of Chinese fir plantations in the case study, the paper explored the current status and development trend of the landscape pattern of Chinese fir plantations after 40 years of manual intervention and natural succession. The paper, based on the three-period survey data on forest resources in 2010, 2015, and 2020, analyzed the dynamic changes of the landscape pattern of Chinese fir plantations from 2010 to 2020 and, by using a transition matrix and landscape index, simulated and predicted the landscape pattern of Chinese fir plantations in Jiangle County in 2025 by constructing a CA–Markov model with Jiangle County, Fujian Province, China, as the study area. The results showed that the landscape of Chinese fir plantations is the main component of the forest landscape in southern China, accounting for 12%. The landscape quality of Chinese fir plantations degraded, mainly shown in the facts that the Chinese fir plantations were juvenile from 2010 to 2020, and that the young and middle-aged forests became the main part of the landscape of Chinese fir plantations, accounting for 54.8%. The landscape area of Chinese fir plantations showed an increasing trend, which mainly came from other coniferous forests, other woodlands, non-woodlands and non-wood forests, and the replaced Chinese fir plantations were mainly eroded by bamboo forests. The evergreen broad-leaved forests, a kind of zonal vegetation, have been effectively protected in the past 10 years. In the future, the total area of Chinese fir plantations will continue to expand, and a small part of them will continue to be eroded by bamboo forests. In order to improve the landscape quality of Chinese fir plantations, it is necessary to adjust the age group structure of Chinese fir plantations, expand the proportion of mature forests, and, meanwhile, continue to protect evergreen broad-leaved forests and curb the expansion of bamboo forests. [ABSTRACT FROM AUTHOR]

Based on the land use data changes in Jilin Province in 2011, 2014, 2017, and 2020, this paper analyzes the land use changes during 2011–2020 through the land use transfer matrix, calculates the changes in carbon sinks of recent years, and then uses the CA–Markov model to predict the land use types and carbon sinks in Jilin Province in 2030 and discusses the driving factors. The results show that cultivated land and forest land are the two major land use types in Jilin Province, and the area of cultivated land, water bodies, and artificial ground in the province increased from 2011 to 2020; the increased area of artificial ground was mainly converted from cultivated land, accounting for 70.34% of the total converted area. The area of forest land is mainly converted along with the area of cultivated land, and grassland is mainly converted to arable areas, accounting for 84.96% of the total converted area. Water bodies and wasteland are mainly converted to cropland and artificial ground, and the area of artificial ground undergoing transfer is smaller. The change in carbon sinks mainly comes from woodland carbon sinks and grassland carbon sinks. In 2030, compared with 2020, the area of woodland, grassland, and wasteland and the corresponding carbon sink is predicted to decrease, among which the area and carbon sink of woodland decrease the most. The factors for land use type change include the slope factor, road factor, township center, and socio-economic drivers. [ABSTRACT FROM AUTHOR]

The land use change (LUC) and urbanization caused by human activities have markedly increased the occurrence of landslides, presenting significant challenges in accurately predicting landslide susceptibility despite decades of model advancements. This study, focusing on the Li River Valley (LRV) within the Yongding District, China, employs two common models, namely an analytic hierarchy process–comprehensive index (AHP-CI) model and a logistic regression (LR) model to assess landslide susceptibility (LS). The AHP-CI model is empirically based, with the advantage of being constructible and applicable at various scales without a dataset, though it remains highly subjective. The LR model is a statistical model that requires a training set. The two models represent heuristic and statistical approaches, respectively, to assessing LS. Meanwhile, the basic geological and environmental conditions are considered in the AHP-CI model, while the LR model accounts for the conditions of LUC and urbanization. The results of the multicollinearity diagnostics reflect the rationality of the predisposing factor selection (1.131 < VIF < 4.441). The findings reveal that the AHP-CI model underperforms in LUC and urbanization conditions (AUROC = 0.645, 0.628, and 0.667 for different validation datasets). However, when all the time-varying human activity predisposing factors are considered, the LR model (AUROC = 0.852) performs significantly better under the conditions of solely considering 2010 (AUROC = 0.744) and 2020 (AUROC = 0.810). The CA–Markov model was employed to project the future land use for the short-term (2025), mid-term (2030), and long-term (2040) planning periods. Based on these projections, maps of future LS were created. Importantly, this paper discussed the relationships between landslide management and regional sustainable development under the framework of the UN SDGs, which are relevant to Goal 1, Goal 11, Goal 13, and Goal 15. Finally, this study highlights the importance of integrating strategic land planning, reforestation efforts, and a thorough assessment of human impact predisposing factors with SDG-aligned LS predictions, advocating for a comprehensive, multi-stakeholder strategy to promote sustainable landslide mitigation. [ABSTRACT FROM AUTHOR]

It is important to understand how changing climate and Land Use Land Cover (LULC) will impact future spatio-temporal water availability across the Munneru river basin as it aids in effective water management and adaptation strategies. The Munneru river basin is one of the important sub-basins of the Krishna River in India. In this paper, the combined impact of LULC and Climate Change (CC) on Munneru water resources using the Soil and Water Assessment Tool (SWAT) is presented. The SWAT model is calibrated and validated for the period 1983–2017 in SWAT-CUP using the SUFI2 algorithm. The correlation coefficient between observed and simulated streamflow is calculated to be 0.92. The top five ranked Regional Climate Models (RCMs) are ensembled at each grid using the Reliable Ensemble Averaging (REA) approach. Predicted LULC maps for the years 2030, 2050 and 2080 using the CA-Markov model revealed increases in built-up and kharif crop areas and decreases in barren lands. The average monthly streamflows are simulated for the baseline period (1983–2005) and for three future periods, namely the near future (2021–2039), mid future (2040–2069) and far future (2070–2099) under Representation Concentration Pathway (RCP) 4.5 and 8.5 climate change scenarios. Streamflows increase in three future periods when only CC and the combined effect of CC and LULC are considered under RCP 4.5 and RCP 8.5 scenarios. When compared to the CC impact in the RCP 4.5 scenario, the percentage increase in average monthly mean streamflow (July–November) with the combined impact of CC and LULC is 33.9% (near future), 35.8% (mid future), and 45.3% (far future). Similarly, RCP 8.5 increases streamflow by 33.8% (near future), 36.5% (mid future), and 38.8% (far future) when compared to the combined impact of CC and LULC with only CC. When the combined impact of CC and LULC is considered, water balance components such as surface runoff and evapotranspiration increase while aquifer recharge decreases in both scenarios over the three future periods. The findings of this study can be used to plan and develop integrated water management strategies for the basin with projected LULC under climate change scenarios. This methodology can be applied to other basins in similar physiographic regions. [ABSTRACT FROM AUTHOR]

With rapid economic development and urban expansion, China faces a serious imbalance between production, living, and ecological land use, in which the erosion of water ecological space by urban expansion is especially notable. In order to alleviate or solve this imbalance, this study constructs the water ecological space in the Mulan River Basin based on national land spatial planning using remote sensing statistics and the 2000–2020 statistical yearbooks for the Mulan River Basin. A landscape index is applied to explore this landscape in terms of its production–living–ecological space (PLES) patterns and evolutionary characteristics. Factors affecting the drivers of PLES changes are analyzed through Geo-Detector, and predictions are made using the cellular automata Markov (CA-Markov) model. It was found that (1) PLES distribution patterns in the Mulan River Basin from 2000 to 2020 are dominated by non-watershed ecological spaces, with a significant expansion of living space. Its ecological space is shrinking, and there is significant spatial variation between its near-river and fringe areas. (2) Of the PLES conversions, the most dramatic conversions are those of production space and living space, with 81.14 km2 of production space being transferred into living space. Non-water ecological space and water ecological space are also mainly transferred into production space. (3) As shown by the results of the landscape index calculation, non-water ecological space in the Mulan River Basin is the dominant landscape, the values of the Shannon diversity index (SHDI) and Shannon homogeneity index (SHEI) are small, the overall level of landscape diversity is low, the aggregation index (AI) is high, and the degree of aggregation is obvious. (4) The progressive PLES changes in the Mulan River Basin are influenced by a combination of natural geographic and socioeconomic factors, with the mean population density and mean elevation being the most important factors affecting PLES changes among social and natural factors, respectively. (5) The Kappa coefficient of the CA-Markov model simulation is 0.8187, showing a good simulation accuracy, and it is predicted that the area of water ecological space in the Mulan River Basin will increase by 3.66 km2 by 2030, the area of production space and non-water ecological space will further decrease, and the area of construction land will increase by 260.67 km2. Overall, the aquatic ecological space in the Mulan River Basin has made progress in terms of landscape ecological protection, though it still faces serious erosion. Therefore, attaching importance to the restoration of the water ecological space in the Mulan River Basin, integrating multiple elements of mountains, water, forests, fields, and lakes, optimizing the spatial structure of its PLES dynamics, and formulating a reasonable spatial planning policy are effective means of guaranteeing its ecological and economic sustainable development. This study offers recommendations for and scientific defenses of the logical design of PLES spatial functions in the Mulan River Basin. [ABSTRACT FROM AUTHOR]

The unreasonable development of land use and use of landscape patterns are the primary causes of the unsustainable growth of tourism cities. Dawa District, a well-known tourist destination in China, was chosen as the research object in order to scientifically evaluate and simulate the dynamic changes in land use. Landsat remote sensing images of the Dawa District from 2011, 2016, and 2021 were processed, using the development of regional tourism in 2016 as the dividing point. Using Arcgis10.3, ENVI5.3, and Fragstats tools, GIS spatial analysis and landscape index analysis were used to explore the spatiotemporal dynamic characteristics of land use and landscape patterns. The CA-Markov model was used to simulate and predict the land use landscape pattern in 2026. The results show that the rapid expansion of built-up areas in 2011–2021 and the increasing marginal complexity and fragmentation of landscape patterns are the main evolution trends. In 2016, emphasis was placed on sustainable land resources, and the area of forested water gradually recovered. However, the patch density (PD) and number of patches (NP) indices reflecting fragmentation still showed a 1.5–2 fold increase. It is expected that in 2026, the dual pressure of urban development and ecological sustainable construction will enhance connectivity and aggregation, and the ecological environment will gradually recover. At the same time, the competition for various types of land will become increasingly prominent, and agricultural activities will be affected. In summary, optimization strategies are proposed from the perspectives of government, land, landscape, and industry, aiming to promote the sustainable development of land and landscape resources in tourist cities. [ABSTRACT FROM AUTHOR]

Simulating the change in ecosystem service values (ESVs) caused by land use changes in metropolitan areas under multiple scenarios is of great significance to ensure regional ecological security and sustainable urban development. This study assessed the variations in land use and ESV in the main urban area of Hangzhou, China, from 2000 to 2020. A total of four future land use scenarios for 2030 were created using the cellular automata–Markov prediction model and ESVs were calculated for four future scenarios. The results are as follows: (1) Arable land and construction land were the most drastic types of land use changes in the main urban area of Hangzhou during the study period. From 2000 to 2020, construction land in the main urban area of Hangzhou expanded rapidly, with an increase of 46,916.82 hm2, while the cultivated land area decreased significantly by 38,396.43 hm2. (2) The ESV in the study area continuously declined from 2000 to 2020, with high-value ecosystem service areas predominantly found in forest areas and water areas. (3) The values of ecosystem services under the simulated future scenarios of natural development, rapid development, planned development, and ecological development were CNY 15.053, 14.525, 16.690, and 16.799 billion, respectively. The planned development and ecological development scenarios effectively ensure a high ESV. The results indicated that balancing various types of land use is essential to guarantee ecological security in urban development. Moreover, urban development and construction should be undertaken in areas with a low ESV. This forecasting study can serve as a key reference for policy makers regarding the urban landscape sustainability of Hangzhou City. The integrated simulation method of land use and ESV proposed in this study can shed light on the urban spatial layout and spatial regulation in urban land use planning. [ABSTRACT FROM AUTHOR]

The Chishui River Basin is located in the bordering area of Yunnan, Guizhou and Sichuan provinces, which serves as an important ecological barrier in the upper reaches of the Yangtze River, and plays a leading role in preserving natural environments, protecting water resources, and maintaining soil functions. However, the eco-environmental quality in the basin has encountered serious challenges in recent years, and the conflict between eco-environmental protection and economic development becomes increasingly prominent. Therefore, it is particularly important to quantitatively assess the extent of the eco-environmental changes in this basin. The present study acquired Landsat series remote sensing images based on the Google Earth Engine (GEE) platform, constructed a remote sensing ecological index (RSEI) as the assessment index that reflects the eco-environmental quality using principal component analysis, studied the changing trend in the eco-environmental quality using the Sen–Mann–Kendall trend test, analyzed the spatial clustering distribution patterns of the eco-environmental quality, based on spatial autocorrelation analysis, and applied the geographical detector model to determine the impacts of natural and anthropogenic factors on the eco-environmental quality. We further applied the CA–Markov model to simulate and predict the eco-environmental quality of the basin in 2025. The results showed the following: (1) between 2000 and 2020, the eco-environmental quality of the Chishui River Basin had been greatly improved. The average RSEI value increased from 0.526 in 2000 to 0.668 in 2020, and the percentage of areas belonging to the good or excellent quality category increased from 42.65% to 68.48%. (2) The main drivers of the eco-environmental quality included population density, mean annual temperature, land use type and elevation. The interactive effect between these drivers was significantly higher than that of individual drivers, and thus possessed stronger explanatory power for quality differences. (3) It is predicted that in 2025, the eco-environmental quality of the basin will continue to improve, and the proportion of land areas with good or excellent quality will continuously increase. The present study can provide reference value for local environmental protection and regional planning. [ABSTRACT FROM AUTHOR]

The evaluation of habitat quality and its genesis is of great significance to ecological protection of the watershed. Based on land use data, Digital Elevation Model (DEM), and road network data and population data, the Cellular Automata (CA)-Markov model and InVEST model were used to analyzed the land use change in the Wanhe Watershed, predicting the land use in 2025. Based on this, the degree of the habitat degradation and habitat quality in 2000, 2005, 2010, 2015, 2020, and 2025 were predicted and analyzed, and combined with the particularity of the terrain in the study area, the topography was introduced. Landform relief gradient was used to discuss the relationship between habitat quality and topographic factors in the Wanhe Watershed, and to reveal the distribution law. The result shows that from 2000 to 2025, farmland and forestland are the main land use types in the study area, and the main change is due to the expansion of the construction land, whereby the area increased by 62.86 km2, with an increase of 34.41%, mainly from farmland and forestland. From 2000 to 2020, mainly due to the expansion in urban land and the reduction in forestland, the habitat degradation tends to be serious, and the habitat quality generally shows a downward trend, with areas with low habitat quality having had the largest increase from 452.67 km² in 2000 to 526.15 km² in 2025. The topography of the study area affects the distribution of natural landscapes and the intensity of human activities, resulting in significant differences in the landscape pattern of habitat degradation and habitat quality. The western mountains are relatively better. After 2020, due to the implementation of environmental protection policies, the habitat quality has tended to improve. This study can promote the adjustment of land use planning policies in the study area, maintain the biodiversity in the watershed, and realize the coordinated development of environmental benefits and social and economic development. The research results have theoretical significance and practical value for ecological environmental protection and land use layout in Wanhe Watershed. [ABSTRACT FROM AUTHOR]

Despite the rapid economic and population growth, the risks related to the current dynamics of land use and land cover (LULC) have attracted a lot of attention in Ethiopia. Therefore, a complete investigation of past and future LULC changes is essential for sustainable water resources and land-use planning and management. Since the 1980s, LULC change has been detected in the upper stream of the Awash River basin. The main purpose of this research was to investigate the current dynamics of LULC and use the combined application of the cellular automata and the Markov chain (CA–Markov) model to simulate the year 2038 LULC in the future; key informant interviews, household surveys, focus group discussions, and field observations were used to assess the consequences and drivers of LULC changes in the upstream Awash basin (USAB). This research highlighted the importance of remote sensing (RS) and geographic information system (GIS) techniques for analyzing the LULC changes in the USAB. Multi-temporal cloud-free Landsat images of three sequential data sets for the periods (1984, 2000, and 2019) were employed to classify based on supervised classification and map LULC changes. Satellite imagery enhancement techniques were performed to improve and visualize the image for interpretation. ArcGIS10.4 and IDRISI software was used for LULC classification, data processing, and analyses. Based on Landsat 5 TM-GLS 1984, Landsat 7 ETM-GLS 2000, and Landsat 8 2019 OLI-TIRS, the supervised maximum likelihood image classification method was used to map the LULC dynamics. Landsat images from 1984, 2000, and 2019 were classified to simulate possible LULC in 2019 and 2038. The result reveals that the maximum area is covered by agricultural land and shrubland. It showed, to the areal extent, a substantial increase in agricultural land and urbanization and a decrease in shrubland, forest, grassland, and water. The LULC dynamics showed that those larger change rates were observed from forest and shrubland to agricultural areas. The results of the study show the radical changes in LULC during 1984–2019; the main reasons for this were agricultural expansion and urbanization. From 1984 to 2019, agriculture increased by 62%, urban area increased by 570.5%, and forest decreased by 88.7%. In the same year, the area of shrubland decreased by 68.6%, the area of water decreased by 65.5%, and the area of grassland decreased by 57.7%. In view of the greater increase in agricultural land and urbanization, as well as the decrease in shrubland, it means that the LULC of the region has changed. This research provides valuable information for water resources managers and land-use planners to make changes in the improvement of future LULC policies and development of sub-basin management strategies in the context of sustainable water resources and land-use planning and management. [ABSTRACT FROM AUTHOR]

In recent years, ecosystem service values (ESV) have attracted much attention. However, studies that use ecological sensitivity methods as a basis for predicting future urban expansion and thus analyzing spatial-temporal change of ESV are scarce in the region. In this study, we used the CA-Markov model to predict the 2030 urban expansion under ecological sensitivity in the Three Gorges Reservoir area based on multi-source data, estimations of ESV from 2000 to 2018 and predictions of ESV losses from 2018 to 2030. Research results: (i) In the concept of green development, the ecological sensitive zone has been identified in Three Gorges Reservoir area; it accounts for about 35.86% of the study area. (ii) It is predicted that the 2030 urban land will reach 211,412.51 ha by overlaying the ecological sensitive zone. (iii) The total ESV of Three Gorges Reservoir area showed an increasing trend from 2000 to 2018 with growth values of about USD 3644.26 million, but the ESVs of 16 districts were decreasing, with Dadukou and Jiangbei having the highest reductions. (iv) New urban land increases by 80,026.02 ha from 2018 to 2030. The overall ESV losses are about USD 268.75 million. Jiulongpo, Banan and Shapingba had the highest ESV losses. [ABSTRACT FROM AUTHOR]

Land use/cover change (LUCC) is one of the causes of global climate and environmental change. Understanding rapid LUCC in urbanized areas is vital for natural resources management for sustainable development. This study primarily considered Vientiane, the capital of Laos, which experienced rapid LUCC due to both natural and anthropogenic factors. The study used geographical information system (GIS) combined with ERDAS and TerrSet technologies to objectively process the ground surveyed and remotely obtained data in order to investigate the historical LUCC as well as predict future LUCC in the study area during the periods of 1995–2018 and 2030–2050, respectively. A comprehensive list of assessment factors comprised of both natural and anthropogenic factors was used for analysis using the cellular automata–Markov (CA–Markov) model. The results show a historical loss of intact forest of 24.36% and of bare land of 1.01%. There were also tremendous increases in degraded forest (11.36%), agricultural land (8.91%), built-up areas (4.49%) and water bodies (1.16%). Finally, the LUCC prediction results indicate the conversion of land use from one type to another, particularly from natural to anthropogenic use, in the near future. These changes demonstrate that the losses associated with ecosystem services will destructively impact human wellbeing in the city and other areas of the country. The study results provide the basic scientific knowledge for LUCC planners, urban designers and natural resources managers. They serve as a decision-making support tool for the establishment of sustainable land resource utilization policies in Vientiane and other cities of similar conditions. [ABSTRACT FROM AUTHOR]

For urban climatic environments, the urban heat island (UHI) effect resulting from land use and land cover change (LUCC) caused by human activities is rapidly becoming one of the most notable characteristics of urban climate change due to urban expansion. UHI effects have become a significant barrier to the process of urbanization and sustainable development of the urban ecological environment. Predicting the spatial and temporal patterns of the urban heat environment from the spatial relationship between land use and land surface temperature (LST) is key to predicting urban heat environment risk. This study established an Urban Heat Environment Risk Model (UHERM) as follows. First, the urban LST was normalized and classified during three different periods. Second, a Markov model was constructed based on spatio-temporal change in the urban heat environment between the initial year (2005) and middle year (2010), and then a cellular automata (CA) model was used to reveal spatial relationships between the urban heat environments of the two periods and land use in the initial year. The spatio-temporal pattern in a future year (2015) was predicted and the accuracy of the simulation was verified. Finally, the spatio-temporal pattern of urban heat environment risk was quantitatively forecasted based on the decision rule for the urban heat environment risk considering both the present and future status of the spatial characteristics of the urban heat environment. The MODIS LST product and LUCC dataset retrieved from remote sensing images were used to verify the accuracy of UHERM and to forecast the spatio-temporal pattern of urban heat environment risk during the period of 2015–2020. The results showed that the risk of urban heat environment is increasing in the Chongqing metropolitan area. This method for quantitatively evaluating the spatio-temporal pattern of urban heat environment risk could guide sustainable growth and provide effective theoretical and technical support for the regulation of urban spatial structure to minimize urban heat environment risk. [ABSTRACT FROM AUTHOR]

Simulating future land use/cover changes is of great importance for urban planners and decision-makers, especially in metropolitan areas, to maintain a sustainable environment. This study examines the changes in land use/cover in the Tokyo metropolitan area (TMA) from 2007 to 2017 as a first step in using supervised classification. Second, based on the map results, we predicted the expected patterns of change in 2027 and 2037 by employing a hybrid model composed of cellular automata and the Markov model. The next step was to decide the model inputs consisting of the modeling variables affecting the distribution of land use/cover in the study area, for instance distance to central business district (CBD) and distance to railways, in addition to the classified maps of 2007 and 2017. Finally, we considered three scenarios for simulating land use/cover changes: spontaneous, sub-region development, and green space improvement. Simulation results show varied patterns of change according to the different scenarios. The sub-region development scenario is the most promising because it balances between urban areas, resources, and green spaces. This study provides significant insight for planners about change trends in the TMA and future challenges that might be encountered to maintain a sustainable region. [ABSTRACT FROM AUTHOR]